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B02 Regression Data Visualization And Eda Bokeh

Table of Contents

Data Description

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.

Imports 

import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)

import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

import os
import time

# random state
random_state=100
np.random.seed(random_state) # we need this in each cell
np.random.set_state=random_state

# Jupyter notebook settings for pandas
pd.set_option('display.max_columns', 50)
pd.set_option('display.float_format', '{:,.2g}'.format) # numbers sep by comma
pd.set_option('display.max_rows', 20) # None for all the rows
pd.set_option('display.max_colwidth', 50)

import IPython
from IPython.display import display

print([(x.__name__,x.__version__) for x in [np, pd,sns,matplotlib]])

[('numpy', '1.16.4'), ('pandas', '0.25.0'), ('seaborn', '0.9.0'), ('matplotlib', '3.1.1')]

%load_ext autoreload
%autoreload 2

%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;

import bokeh
from bokeh.io import output_file, output_notebook
from bokeh.plotting import figure, show, reset_output
from bokeh.models import ColumnDataSource
from bokeh.layouts import row, column, gridplot
from bokeh.models.widgets import Tabs, Panel
from bokeh.palettes import Spectral6
from bokeh.models import ColumnDataSource,FactorRange

# Output the visualization directly in the notebook
output_notebook()

[(x.__name__,x.__version__) for x in [bokeh]]
Loading BokehJS ... 
[('bokeh', '1.3.4')]
%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;

Useful Scripts 

def show_method_attributes(method, ncols=7):
    """ Show all the attributes of a given method.
    Example:
    ========
    show_method_attributes(list)
     """
    x = [i for i in dir(method) if i[0]!='_']
    x = [i for i in x if i not in 'os np pd sys time psycopg2'.split()]

    return pd.DataFrame(np.array_split(x,ncols)).T.fillna('')

Load the data 

df = pd.read_csv('../data/processed/data_cleaned_encoded.csv')
print(df.shape)
df.head().T

(21613, 92)
0 1 2 3 4
id 7129300520 6414100192 5631500400 2487200875 1954400510
date 2014-10-13 2014-12-09 2015-02-25 2014-12-09 2015-02-18
price 2.2e+05 5.4e+05 1.8e+05 6e+05 5.1e+05
bedrooms 3 3 2 4 3
bathrooms 1 2.2 1 3 2
sqft_living 1180 2570 770 1960 1680
sqft_lot 5650 7242 10000 5000 8080
floors 1 2 1 1 1
waterfront 0 0 0 0 0
view 0 0 0 0 0
... ... ... ... ... ...
age_after_renovation_cat_7 0 0 1 0 0
age_after_renovation_cat_8 0 0 0 0 0
age_after_renovation_cat_9 0 0 0 0 0
log1p_price 12 13 12 13 13
log1p_sqft_living 7.1 7.9 6.6 7.6 7.4
log1p_sqft_lot 8.6 8.9 9.2 8.5 9
log1p_sqft_above 7.1 7.7 6.6 7 7.4
log1p_sqft_basement 0 6 0 6.8 0
log1p_sqft_living15 7.2 7.4 7.9 7.2 7.5
log1p_sqft_lot15 8.6 8.9 9 8.5 8.9

92 rows × 5 columns

Univariate Analysis

Discrete variables and Categorical variables 

df['bedrooms'].value_counts()

3     9824
4     6882
2     2760
5     1601
6      272
1      199
7       38
8       13
0       13
9        6
10       3
11       1
33       1
Name: bedrooms, dtype: int64
from bhishan.util_bokeh import countplot_bokeh

ofile = '../reports/bokeh_outputs/bedrooms_countplot.html'
countplot_bokeh(df, 'bedrooms',height=400,ofile=None)
Loading BokehJS ... 
countplot_bokeh(df,'view',height=300)
Loading BokehJS ...

Continuous variables

histograms 

from bhishan.util_bokeh import histogram_bokeh

histogram_bokeh(df,'sqft_living',n_bins=20)
Loading BokehJS ...

Bi-variate Analysis

Scatter plots 

from bhishan.util_bokeh import scatterplot_bokeh

ofile = '../reports/bokeh_outputs/sqftLiving_vs_price.html'
scatterplot_bokeh(df,'sqft_living','price',ofile=ofile)

Multi-variate Analysis

countplots 

from bhishan.util_bokeh import stacked_countplot_bokeh

stacked_countplot_bokeh(df,'bedrooms','yr_sales','price')
Loading BokehJS ...

Map visualization 

df[['lat','long']].head()
lat long
0 48 -1.2e+02
1 48 -1.2e+02
2 48 -1.2e+02
3 48 -1.2e+02
4 48 -1.2e+02 
from bhishan.util_bokeh import map_plot_bokeh

ofile = '../reports/bokeh_outputs/map.html'
map_plot_bokeh(df, 'lat', 'long',ofile)